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000892373 1001_ $$0P:(DE-Juel1)177675$$aUpschulte, Eric$$b0$$eCorresponding author$$ufzj
000892373 245__ $$aContour Proposal Networks for Biomedical Instance Segmentation
000892373 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2022
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000892373 520__ $$aWe present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available.
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000892373 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
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000892373 7001_ $$0P:(DE-HGF)0$$aHarmeling, Stefan$$b1
000892373 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
000892373 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000892373 773__ $$0PERI:(DE-600)1497450-2$$gp. 102371 -$$p102371 -$$tMedical image analysis$$x1361-8415$$y2022
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